I'm writing a code to test this, but why do you ask this ? Is there a case 
where nvidia-smi might give me 35% util when the GPU is actually running 
the code as fast as it can ?

On Wednesday, November 9, 2016 at 5:36:14 PM UTC-7, Michael Klachko wrote:
>
> Ragav, so when GPU is 98% utilized, is the training faster than when it's 
> 35% utilized? Have you timed it?
>
> On Wed, Nov 9, 2016 at 4:09 PM, Ragav Venkatesan <[email protected] 
> <javascript:>> wrote:
>
>> After investigating further I don't think this is a speed or slow issue. 
>> I think the newer version of CUDA/cuDNN using the cuda backend is not using 
>> the GPU fully. The older version (7.5/5103) of CUDA/cuDNN produce 98% GPU 
>> util but the same code on the latest versions (8.0/5105) don't. The code by 
>> the way is the lenet tutorial from theano, so its not some weird coding 
>> error also. Using the libgpuarray backend, I am able to produce 98% util 
>> even with CUDA/cuDNN (8/5105).
>>
>> On Wednesday, November 9, 2016 at 9:48:40 AM UTC-7, nouiz wrote:
>>>
>>> It could be that the new back-end (libgpuarray) is faster and more 
>>> efficient in that cases. So just use that back-end :)
>>>
>>> The speed difference between both back-end isn't constant, but should be 
>>> a little bit faster with the new back-end in average.
>>>
>>> We have found a few speed regression in the new back-end, but they where 
>>> fixed. If you found one, just tell us and we'll fix it. But the probably is 
>>> still low of having slowdown in the new back-end.
>>>
>>> We just merged one such fix with indexing. Make sure to update 
>>> libgpuarray and recompile it if you want to be sure to have the fastest 
>>> version.
>>>
>>> Fred
>>>
>>> On Tue, Nov 8, 2016 at 1:56 PM, Ragav Venkatesan <[email protected]> 
>>> wrote:
>>>
>>>> Ok, here is a problem I'm getting and I am not sure how to solve this. 
>>>> If I use the libgpuarray backend on the cnn_tutorial I am getting a 98% 
>>>> gpu 
>>>> tutilization with cudnn 5105. If I use cuda backend, I am only getting 
>>>> about 35% utilization. 
>>>> Anyidea why this might be so ?
>>>>
>>>> On Monday, October 24, 2016 at 9:38:17 AM UTC-7, nouiz wrote:
>>>>>
>>>>> What errors do you have? Delete your Theano cache, just in case and be 
>>>>> sure to use Theano dev version. The last release don't support it I think.
>>>>>
>>>>> Fred
>>>>>
>>>>> On Mon, Oct 24, 2016 at 12:33 PM, Michael Klachko <
>>>>> [email protected]> wrote:
>>>>>
>>>>>> Yes, it's supported, I'm using it right now (CUDA 8.0 on Ubuntu 
>>>>>> 14.04):
>>>>>>
>>>>>> >>> import theano
>>>>>> Using gpu device 0: TITAN X (Pascal) (CNMeM is enabled with initial 
>>>>>> size: 30.0% of memory, cuDNN 5105)
>>>>>> >>> print theano.__version__
>>>>>> 0.9.0dev3.dev-20fd30a38d34687e9d944140042762ca9fca6276
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Saturday, October 22, 2016 at 2:54:00 PM UTC-7, Ragav Venkatesan 
>>>>>> wrote:
>>>>>>>
>>>>>>> I updated and I'm getting some weird errors. With Cuda backend, 
>>>>>>> convolutions only run on CPU and with libgpuarray backend GPUs only run 
>>>>>>> at 
>>>>>>> about 35% util. 
>>>>>>>
>>>>>>>
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>>>>>
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